Markerless Motion Capture for 3D Clinical Gait Analysis (IMAGine)

NCT07572214 | Completed

• 1 other identifier: 334786
• Study Type: observational
• Target: 100
• Locations: 1 country
• Sites: 1

Brief Summary

Many children have trouble with walking and frequently trip and fall. To understand the extent of the changes in their walking, but also to inform and evaluate possible interventions to improve their walking, a gait analysis is carried out. Gait analysis is the assessment of how someone walks. It requires the accurate placement of markers on the skin in anatomical locations. The positions of these markers are then measured using infrared cameras and the results are displayed as movement graphs. These graphs are used in clinical decision making. However, this assessment can be time-consuming and uncomfortable for the child, and it may trigger anxiety, leading to an unnatural walking pattern. This style of assessment may also not be suitable for some children who are either very young or have additional sensory needs. One potential solution to these problems is to use motion capture systems that do not require markers. Instead, these systems use high-resolution video cameras to capture human motion. Artificial intelligence is then used to identify human body features from the video footage and produces movement graphs. Known as markerless motion capture, this emerging approach has been developed over the last few years and has demonstrated promising results compared to the current marker-based method in adults and children. However, it is not known how well markerless motion capture works for children with gait abnormalities, which is what this study aims to find out. Therefore, fifty children with movement difficulties will walk barefoot over a walkway with and without markers attached, while markerless and marker-based cameras record all trials simultaneously. This study will then compare the movement graphs created by the markerless and marker-based motion capture systems. The results of this study will also enable Alder Hey to be the lead clinical gait lab user of markerless technology in the UK, helping other gait labs to adopt the technology.

Conditions

Paediatric Patients

Keywords

Gait analysis; markerless movement analysis; Theia3D; movement abnormalities; paediatric; children

Outcome Measures

Primary Outcomes (3)

• To establish if Theia3D and the existing marker-based approach can be used interchangeably to determine lower body and trunk joint angles during overground walking in children.

  Markerless-derived joint angles (measured in degrees) will be compared to the traditional marker-based approach using statistical waveform comparison techniques.

  Baseline

• To establish if Theia3D and the existing marker-based approach can be used interchangeably to determine lower body joint moments during overground walking in children.

  Markerless-derived joint moments (Nm) will be compared to the traditional marker-based approach using statistical waveform comparison techniques.

  Baseline

• To establish if Theia3D and the existing marker-based approach can be used interchangeably to determine temporo-spatial parameters during overground walking in children.

  Markerless-derived temporo-spatial parameters will be compared to the traditional marker-based approach using root mean square differences and paired t-tests.

  Baseline

Secondary Outcomes (5)

• Do clinical experts' interpretation of gait analysis results differ between markerless and marker-based recordings?

  Baseline

• Does marker-application lead to different joint kinematics potentially leading to unnatural walking patterns in children?

  Baseline

• Does marker-application lead to different temporospatial parameters potentially leading to unnatural walking patterns in children?

  Baseline

• Can Theia3D identify lower body joint angles during overground walking in children when the legs and thorax is covered by clothing?

  Baseline

• Can Theia3D identify joint moments during overground walking in children when the legs and thorax is covered by clothing?

  Baseline

Study Arms (1)

Children with gait abnormalities

Children with gait abnormalities

Eligibility Criteria

• Age: 6 Years - 18 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64)
• Sampling Method: Non-Probability Sample

Study Population

Participant referred to the gait lab to carry out gait analysis

You may qualify if:

• Referred to the gait lab to carry out a 3D gait analysis
• Able to cope with the demands of the 3D gait analysis
• Able to comprehend and understand instruction in English in order to consent and safely undertake the clinical gait analysis
• Aged between 6-18 years of age

You may not qualify if:

• Non-ambulatory, unable to walk.
• Aged below 6 years of age
• Unable to comprehend and understand instruction in English in order to consent and safely undertake the clinical gait analysis

Sponsors & Collaborators

• Alder Hey Children's NHS Foundation Trust: lead
• Liverpool John Moores University: collaborator

Study Sites (1)

Alder Hey Children's NHS Foundation Trust

Liverpool, Merseyside, L14 5AB, United Kingdom

Location

Related Publications (7)

• Stewart C, Eve L, Durham S, Holmes G, Stebbins J, Harrington M, Corbett M, Kiernan D, Kidgell V, Jarvis S, Daly C, Noble J. Clinical Movement Analysis Society - UK and Ireland: Clinical Movement Analysis Standards. Gait Posture. 2023 Sep;106:86-94. doi: 10.1016/j.gaitpost.2023.08.006. Epub 2023 Aug 18. No abstract available.

  PMID: 37683446 RESULT

• Wilken JM, Rodriguez KM, Brawner M, Darter BJ. Reliability and Minimal Detectible Change values for gait kinematics and kinetics in healthy adults. Gait Posture. 2012 Feb;35(2):301-7. doi: 10.1016/j.gaitpost.2011.09.105. Epub 2011 Oct 29.

  PMID: 22041096 RESULT

• Kanko RM, Laende EK, Davis EM, Selbie WS, Deluzio KJ. Concurrent assessment of gait kinematics using marker-based and markerless motion capture. J Biomech. 2021 Oct 11;127:110665. doi: 10.1016/j.jbiomech.2021.110665. Epub 2021 Aug 3.

  PMID: 34380101 RESULT

• McGinley JL, Baker R, Wolfe R, Morris ME. The reliability of three-dimensional kinematic gait measurements: a systematic review. Gait Posture. 2009 Apr;29(3):360-9. doi: 10.1016/j.gaitpost.2008.09.003. Epub 2008 Nov 13.

  PMID: 19013070 RESULT

• Hallemans A, Van de Walle P, Wyers L, Verheyen K, Schoonjans AS, Desloovere K, Ceulemans B. Clinical usefulness and challenges of instrumented motion analysis in patients with intellectual disabilities. Gait Posture. 2019 Jun;71:105-115. doi: 10.1016/j.gaitpost.2019.04.016. Epub 2019 Apr 22.

  PMID: 31039461 RESULT

• Hulleck AA, Menoth Mohan D, Abdallah N, El Rich M, Khalaf K. Present and future of gait assessment in clinical practice: Towards the application of novel trends and technologies. Front Med Technol. 2022 Dec 16;4:901331. doi: 10.3389/fmedt.2022.901331. eCollection 2022.

  PMID: 36590154 RESULT

• Cimolin V, Galli M. Summary measures for clinical gait analysis: a literature review. Gait Posture. 2014 Apr;39(4):1005-10. doi: 10.1016/j.gaitpost.2014.02.001. Epub 2014 Feb 7.

  PMID: 24613461 RESULT

MeSH Terms

Conditions

Movement Disorders

Condition Hierarchy (Ancestors)

Central Nervous System Diseases; Nervous System Diseases

Study Officials

• Richard J Foster, BSc (hons), MSc, PhD

  Liverpool John Moores University

  PRINCIPAL INVESTIGATOR

• Joel Kearney, BSc, MSc

  Liverpool John Moores University

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: July 7, 2025
• First Posted: May 7, 2026
• Study Start: March 31, 2025
• Primary Completion: December 22, 2025
• Study Completion: December 22, 2025
• Last Updated: May 7, 2026

Record last verified: 2025-07

Data Sharing

• IPD Sharing: Will not share

Locations

GB (1)